Cylindrical label defect detection based on machine vision

Yong Xin Zhao; Qing Hua Zhou

doi:10.1117/12.3004998

20 October 2023 Cylindrical label defect detection based on machine vision

Yong Xin Zhao, Qing Hua Zhou

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 129161R (2023) https://doi.org/10.1117/12.3004998
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

In the pharmaceutical industry, the label on vials of antibiotics contains crucial information about the medication. The quality of the label's information determines significant issues such as drug classification, storage, safe usage, and also affects the economic benefits of pharmaceutical manufacturers. In response to the shape distortion that may occur in cylindrical labels, this paper proposes a cylindrical back projection correction algorithm. The adjacent label images are stitched together after cylindrical back projection to create a complete cylindrical label image. The corrected and stitched label image is then used as the input dataset for defect detection using the ResNet-18 residual network. A comparison is made between the defect detection results obtained from the corrected and uncorrected label images. The feasibility of the algorithm is validated by comparing the accuracy of the two sets of experimental results. The experimental results demonstrate that the proposed algorithm improves the accuracy of cylindrical label defect detection.

Citation Download Citation

Yong Xin Zhao and Qing Hua Zhou "Cylindrical label defect detection based on machine vision", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 129161R (20 October 2023); https://doi.org/10.1117/12.3004998

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KEYWORDS

Cameras

Defect detection

Distortion

Imaging systems

Image fusion

Image processing

Content addressable memory

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